Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add PANGKAIFENG/ai-product-manager-skills --skill solution-loopgit clone --depth 1 https://github.com/PANGKAIFENG/ai-product-manager-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/pangkaifeng/ai-product-manager-skills/solution-loop)<a href="https://agentmods.dev/skills/pangkaifeng/ai-product-manager-skills/solution-loop"><img src="https://agentmods.dev/badge/skills/pangkaifeng/ai-product-manager-skills/solution-loop/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/pangkaifeng/ai-product-manager-skills/solution-loop"><img src="https://agentmods.dev/badge/skills/pangkaifeng/ai-product-manager-skills/solution-loop.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00081 | $0.00551 |
| Opus 5 | $0.00041 | $0.00275 |
| Sonnet 5 | $0.00016 | $0.00110 |
| Haiku 4.5 | $0.00008 | $0.00055 |
Grade A, and why
solution-loop scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
方案闭环
这是 loop 的 Codex Runtime 入口，不是新的方案 Skill。先读取同目录 LOOP.md，再使用 brainstorming 和 grill-me 关闭方案中的关键缺口。
目标与输入
目标是关闭候选方案的关键缺口，而不是不断重写整个方案。Entry Gate 必须已有可识别版本的候选方案、范围、关键流程和风险。没有候选方案时使用 brainstorming；问题和目标仍模糊时使用 $problem-to-solution。
工作流
- 建立或恢复 Loop 状态，
max_cycles固定为 3。 - 使用
grill-me找到最早、最关键且可修复的因果缺口，生成一个 Challenge Record。 - 使用
brainstorming只修改该 challenge 影响的部分，输出 Design Delta 和保留项。 - 把同一 challenge 交回
grill-me复核；Maker 不得自批 clear。 - 当前 challenge 关闭后，判断方案是否确认、是否还有一个新的阻断 gap，或是否进入 Human Gate。
输出
每轮返回 cycle、方案版本、active challenge、closure criterion、Design Delta、保留项、状态和恢复点。结束时只报告 solution_confirmed、human_gate 或 blocked，不自动写 PRD 或调用外部 Publisher。
完成定义
只有 Critic 依据同一 closure criterion 关闭当前 challenge，且没有新的阻断 gap，才输出 solution_confirmed。三轮上限、连续两轮无有效 Design Delta 或业务取舍不可推断时进入 Human Gate。
资源与验证
LOOP.md是 Maker/Critic 分工、状态字段和停止条件的权威合同，每次执行或恢复前读取。evals/evals.json覆盖标准闭环、cycle 2 恢复、无候选方案分流和轮次上限回归；修改入口后运行这些评测并保留结果。
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 36 lines · 81 tokens per session scan A 154dab257835
solution-loop is a skill published in the GitHub repository PANGKAIFENG/ai-product-manager-skills (11 stars, last pushed 12d ago), licensed MIT. It adds 81 tokens to every session and 551 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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